add values to each array in a 2D numpy array

Question

I'm looking for a way to efficiently add a fixed number of np.nan values at the beginning of each array in a numpy array of shape (3,123...):

Original numpy array:

[[1,2,3...],
 [4,5,6...],
 [7,8,9...]]

New numpy array:

[[nan,nan,1,2,3...],
 [nan,nan,4,5,6...],
 [nan,nan,7,8,9...]]

I tried with a for loop:

import numpy as np

original_array = np.random.rand(3,3)
gap = np.empty(shape=(2,))
gap.fill(np.nan)
new_array = np.zeros(shape=(3,5))
for i,row in enumerate(original_array):
    new_array[i] = np.concatenate([gap,row])

It works but this is probably not the best way to do it.

Look at np.hstack

roganjosh
– roganjosh

2023-02-07 15:23:21 +00:00
Commented Feb 7, 2023 at 15:23 — roganjosh
– roganjosh, Commented Feb 7, 2023 at 15:23

delirium78 · Accepted Answer · 2023-02-07 15:28:48Z

2

You could also use np.pad

import numpy as np

original_array = np.arange(1, 10).reshape(-1, 3).astype(np.float32)
npad = ((0, 0), (2, 0))
np.pad(original_array,npad,mode='constant',constant_values=(np.nan))

Output:

array([[nan, nan,  1.,  2.,  3.],
       [nan, nan,  4.,  5.,  6.],
       [nan, nan,  7.,  8.,  9.]], dtype=float32)

answered Feb 7, 2023 at 15:28

delirium78

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Comments

PaulS · Accepted Answer · 2023-02-07 15:39:10Z

0

A possible solution (thanks to @roganjosh for suggesting the use of numpy.full), which is based on numpy.hstack:

a = np.full((3,2), np.nan)
b = np.arange(1, 10).reshape(-1, 3)
np.hstack((a, b))

Output:

array([[nan, nan,  1.,  2.,  3.],
       [nan, nan,  4.,  5.,  6.],
       [nan, nan,  7.,  8.,  9.]])

edited Feb 7, 2023 at 15:39

answered Feb 7, 2023 at 15:24

PaulS

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1 Comment

PaulS Over a year ago

Thanks a lot, @roganjosh! np.full is much better and I have edited my solution accordingly.

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